let nn;
const nn_options = {
    inputs: 1,
    outputs: 1,
    layers: [
        ml5.tf.layers.dense({
            units: 16,
            inputShape: [1],
            activation: 'relu',
        }),
        ml5.tf.layers.dense({
            units: 16,
            activation: 'sigmoid',
        }),
        ml5.tf.layers.dense({
            units: 1,
            activation: 'sigmoid',
        })
    ],
    debug: true
}

function setup() {
    createCanvas(400, 400);
    background(240);
    
     $.getScript ( "https://unpkg.com/ml5@0.4.3/dist/ml5.min.js", function()
    {
        $.getScript ( "https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.9.0/addons/p5.sound.min.js", function()
        {
            $.getScript ( "/uploads/codingtrain/mnist.js", function()
            {
                console.log ("All JS loaded");
               // nn = new NeuralNetwork(  noinput, nohidden, nooutput );
            //    nn = ml5.neuralNetwork(nn_options);
                //nn.setLearningRate ( learningrate );
                //loadData();
            });
        });
    });
    nn = ml5.neuralNetwork(nn_options);
    console.log(nn);
    createTrainingData();

    nn.normalizeData();
    const train_options = {
        epochs: 32
    }
    nn.train(train_options, finishedTraining);
}

function finishedTraining(){

    nn.predict([10], function(err, result){
        if(err){
            console.log(err);
            return
        }
        console.log(result)
    })

    nn.predict([390], function(err, result){
        if(err){
            console.log(err);
            return
        }
        console.log(result)
    })
}

function createTrainingData(){
    for(let i = 0; i < 400; i++){
        if(i%2){
            const x = floor(random(0, width/2));
            nn.addData([x], [0])
        }else {
            const x = floor(random(width/2, width));
            nn.addData([x], [1])
        }
    }
    
}